In this notebook we conduct exploratory factor analyses (EFAs) on the datasets for our studies of concepts of mental life, in which each participants judged the various mental capacities of a particular target entity. We analyze datasets for adults and children from each of our five field sites: the US, Ghana, Thailand, China, and Vanuatu.

This notebook contains the results presented in the main text, in which we use Pearson correlations with our three-point response scale (no = 0, kinda = 0.5, yes = 1); see supplemental analyses for a version of these analyses treating kinda = yes = 1 and using tetrachoric correlations.

Adults

Samples

  country   n
       US 127
    Ghana 150
 Thailand 150
    China 136
  Vanuatu 148
    Total 711

Scale use

Factor retention: parallel analysis

Exploratory factor analysis

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.Matrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.Matrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.Matrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.Matrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.convergence not obtained in GPFoblq. 1000 iterations used.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.Matrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.convergence not obtained in GPFoblq. 1000 iterations used.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  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Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.Matrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.convergence not obtained in GPFoblq. 1000 iterations used.convergence not obtained in GPFoblq. 1000 iterations used.convergence not obtained in GPFoblq. 1000 iterations used.convergence not obtained in GPFoblq. 1000 iterations used.convergence not obtained in GPFoblq. 1000 iterations used.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyconvergence not obtained in GPFoblq. 1000 iterations used.convergence not obtained in GPFoblq. 1000 iterations used.convergence not obtained in GPFoblq. 1000 iterations used.convergence not obtained in GPFoblq. 1000 iterations used.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Factor loadings

the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used

Congruence

funs() is soft deprecated as of dplyr 0.8.0
Please use a list of either functions or lambdas: 

  # Simple named list: 
  list(mean = mean, median = median)

  # Auto named with `tibble::lst()`: 
  tibble::lst(mean, median)

  # Using lambdas
  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once per session.

Bootstrapped congruence

Children

Samples

  country   n
       US 117
    Ghana 150
 Thailand 152
    China 131
  Vanuatu 143
    Total 693

Scale use

Factor retention: parallel analysis

Exploratory factor analysis

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Try a different factor score estimation method.convergence not obtained in GPFoblq. 1000 iterations used.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyconvergence not obtained in GPFoblq. 1000 iterations used.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.convergence not obtained in GPFoblq. 1000 iterations used.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.convergence not obtained in GPFoblq. 1000 iterations used.convergence not obtained in GPFoblq. 1000 iterations used.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.convergence not obtained in GPFoblq. 1000 iterations used.convergence not obtained in GPFoblq. 1000 iterations used.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.convergence not obtained in GPFoblq. 1000 iterations used.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.convergence not obtained in GPFoblq. 1000 iterations used.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.convergence not obtained in GPFoblq. 1000 iterations used.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyconvergence not obtained in GPFoblq. 1000 iterations used.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyconvergence not obtained in GPFoblq. 1000 iterations used.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.convergence not obtained in GPFoblq. 1000 iterations used.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.convergence not obtained in GPFoblq. 1000 iterations used.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.convergence not obtained in GPFoblq. 1000 iterations used.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.convergence not obtained in GPFoblq. 1000 iterations used.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.convergence not obtained in GPFoblq. 1000 iterations used.convergence not obtained in GPFoblq. 1000 iterations used.convergence not obtained in GPFoblq. 1000 iterations used.convergence not obtained in GPFoblq. 1000 iterations used.

Factor loadings

the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used

Congruence

See All samples, below.

Bootstrapped congruence

All samples

Congruence

the condition has length > 1 and only the first element will be usedVectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.the condition has length > 1 and only the first element will be usedVectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used

Jaccard Similarity

the condition has length > 1 and only the first element will be usedVectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.the condition has length > 1 and only the first element will be usedVectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used

Developmental comparisons

Column `country` joining character vector and factor, coercing into character vector

Column `country` joining character vector and factor, coercing into character vector

Column `country` joining character vector and factor, coercing into character vector

Column `country` joining character vector and factor, coercing into character vector

Column `country` joining character vector and factor, coercing into character vector

Column `country` joining character vector and factor, coercing into character vector

Other metrics of similarity

[1] 0.6086957

Variance accounted for

Interfactor correlations

US ADULTS
          F1        F2        F3
F1 1.0000000 0.5114648 0.4805622
F2 0.5114648 1.0000000 0.5376676
F3 0.4805622 0.5376676 1.0000000
          F1        F2        F3
F1 1.0000000 0.2615963 0.2309400
F2 0.2615963 1.0000000 0.2890864
F3 0.2309400 0.2890864 1.0000000

US CHILDREN
          F1        F2        F3
F1 1.0000000 0.3031865 0.4334098
F2 0.3031865 1.0000000 0.4856172
F3 0.4334098 0.4856172 1.0000000
           F1         F2        F3
F1 1.00000000 0.09192203 0.1878441
F2 0.09192203 1.00000000 0.2358240
F3 0.18784410 0.23582402 1.0000000
GHANA ADULTS
          F1        F2        F3
F1 1.0000000 0.2725881 0.3444798
F2 0.2725881 1.0000000 0.2558207
F3 0.3444798 0.2558207 1.0000000
           F1         F2         F3
F1 1.00000000 0.07430429 0.11866634
F2 0.07430429 1.00000000 0.06544426
F3 0.11866634 0.06544426 1.00000000

GHANA CHILDREN
          F1        F2        F3
F1 1.0000000 0.5790820 0.1747165
F2 0.5790820 1.0000000 0.3854114
F3 0.1747165 0.3854114 1.0000000
           F1       F2         F3
F1 1.00000000 0.335336 0.03052585
F2 0.33533601 1.000000 0.14854196
F3 0.03052585 0.148542 1.00000000
THAILAND ADULTS
          F1        F2        F3
F1 1.0000000 0.4142881 0.3218404
F2 0.4142881 1.0000000 0.4161488
F3 0.3218404 0.4161488 1.0000000
          F1        F2        F3
F1 1.0000000 0.1716346 0.1035813
F2 0.1716346 1.0000000 0.1731798
F3 0.1035813 0.1731798 1.0000000

THAILAND CHILDREN
             F1         F2        F3           F4
F1  1.000000000 0.54189979 0.1468730 -0.008909088
F2  0.541899792 1.00000000 0.3169020  0.092978779
F3  0.146873030 0.31690205 1.0000000  0.269117295
F4 -0.008909088 0.09297878 0.2691173  1.000000000
             F1          F2         F3           F4
F1 1.000000e+00 0.293655384 0.02157169 7.937185e-05
F2 2.936554e-01 1.000000000 0.10042691 8.645053e-03
F3 2.157169e-02 0.100426907 1.00000000 7.242412e-02
F4 7.937185e-05 0.008645053 0.07242412 1.000000e+00
CHINA ADULTS
          F1        F2        F3
F1 1.0000000 0.4590388 0.6187141
F2 0.4590388 1.0000000 0.3703614
F3 0.6187141 0.3703614 1.0000000
          F1        F2        F3
F1 1.0000000 0.2107166 0.3828072
F2 0.2107166 1.0000000 0.1371675
F3 0.3828072 0.1371675 1.0000000

CHINA CHILDREN
          F1         F2        F3         F4
F1 1.0000000 0.51249526 0.3245885 0.25786749
F2 0.5124953 1.00000000 0.1524842 0.07635739
F3 0.3245885 0.15248416 1.0000000 0.13450834
F4 0.2578675 0.07635739 0.1345083 1.00000000
           F1          F2         F3          F4
F1 1.00000000 0.262651390 0.10535767 0.066495642
F2 0.26265139 1.000000000 0.02325142 0.005830451
F3 0.10535767 0.023251420 1.00000000 0.018092495
F4 0.06649564 0.005830451 0.01809249 1.000000000
VANUATU ADULTS
         F1       F2
F1 1.000000 0.687325
F2 0.687325 1.000000
          F1        F2
F1 1.0000000 0.4724157
F2 0.4724157 1.0000000

VANUATU CHILDREN
          F1        F2        F3
F1 1.0000000 0.3116574 0.5189923
F2 0.3116574 1.0000000 0.3362370
F3 0.5189923 0.3362370 1.0000000
           F1         F2        F3
F1 1.00000000 0.09713036 0.2693530
F2 0.09713036 1.00000000 0.1130554
F3 0.26935296 0.11305535 1.0000000
---
title: "Concepts of mental life across cultures: Primary analysis"
authors: "Weisman, Legare, & Luhrmann"
output: 
  html_notebook:
    toc: true
    toc_float: true
---

```{r setup}
knitr::opts_chunk$set(echo = F, message = F)
```

In this notebook we conduct exploratory factor analyses (EFAs) on the datasets for our studies of concepts of mental life, in which each participants judged the various mental capacities of a particular target entity. We analyze datasets for adults and children from each of our five field sites: the US, Ghana, Thailand, China, and Vanuatu. 

This notebook contains the results presented in the main text, in which we use Pearson correlations with our three-point response scale (no = 0, kinda = 0.5, yes = 1); see supplemental analyses for a version of these analyses treating kinda = yes = 1 and using tetrachoric correlations.


```{r, echo = F, message = F}
source("./scripts/dependencies.R")
source("./scripts/custom_funs.R")
source("./scripts/var_recode_contrast.R")
```

```{r data}
# read in data, shorten "feel sick," and limit to universal targets and questions: adults
d_us_adults <- read_csv("../data/d_us_adults.csv") %>%
  filter(target %in% levels_target_univ, question_cat == "universal") %>%
  mutate(question = gsub("\\, .*$", " \\[...\\]", question))
d_gh_adults <- read_csv("../data/d_gh_adults.csv") %>%
  filter(target %in% levels_target_univ, question_cat == "universal") %>%
  mutate(question = gsub("\\, .*$", " \\[...\\]", question))
d_th_adults <- read_csv("../data/d_th_adults.csv") %>%
  filter(target %in% levels_target_univ, question_cat == "universal") %>%
  mutate(question = gsub("\\, .*$", " \\[...\\]", question))
d_ch_adults <- read_csv("../data/d_ch_adults.csv") %>%
  filter(target %in% levels_target_univ, question_cat == "universal") %>%
  mutate(question = gsub("\\, .*$", " \\[...\\]", question))
d_vt_adults <- read_csv("../data/d_vt_adults.csv") %>%
  filter(target %in% levels_target_univ, question_cat == "universal") %>%
  mutate(question = gsub("\\, .*$", " \\[...\\]", question))

# read in data, shorten "feel sick," and limit to universal targets and questions: children
d_us_children <- read_csv("../data/d_us_children.csv") %>%
  filter(target %in% levels_target_univ, question_cat == "universal") %>%
  mutate(question = gsub("\\, .*$", " \\[...\\]", question))
d_gh_children <- read_csv("../data/d_gh_children.csv") %>%
  filter(target %in% levels_target_univ, question_cat == "universal") %>%
  mutate(question = gsub("\\, .*$", " \\[...\\]", question))
# d_gh_eng_children <- read_csv("../data/d_gh_eng_children.csv") %>%
#   filter(target %in% levels_target_univ, question_cat == "universal") %>%
#   mutate(question = gsub("\\, .*$", " \\[...\\]", question))
d_th_children <- read_csv("../data/d_th_children.csv") %>%
  filter(target %in% levels_target_univ, question_cat == "universal") %>%
  mutate(question = gsub("\\, .*$", " \\[...\\]", question))
d_ch_children <- read_csv("../data/d_ch_children.csv") %>%
  filter(target %in% levels_target_univ, question_cat == "universal") %>%
  mutate(question = gsub("\\, .*$", " \\[...\\]", question))
d_vt_children <- read_csv("../data/d_vt_children.csv") %>%
  filter(target %in% levels_target_univ, question_cat == "universal") %>%
  mutate(question = gsub("\\, .*$", " \\[...\\]", question)) %>%
  # filter out participants outside of the age range
  filter((age >= 6 & age <= 12) | is.na(age))
```

```{r wide}
# make wide-form datasets for EFA: adults
d_us_adults_w <- wide_df_fun(d_us_adults)
d_gh_adults_w <- wide_df_fun(d_gh_adults)
d_th_adults_w <- wide_df_fun(d_th_adults)
d_ch_adults_w <- wide_df_fun(d_ch_adults)
d_vt_adults_w <- wide_df_fun(d_vt_adults)

# make wide-form datasets for EFA: children
d_us_children_w <- wide_df_fun(d_us_children)
d_gh_children_w <- wide_df_fun(d_gh_children)
# d_gh_eng_children_w <- wide_df_fun(d_gh_eng_children)
d_th_children_w <- wide_df_fun(d_th_children)
d_ch_children_w <- wide_df_fun(d_ch_children)
d_vt_children_w <- wide_df_fun(d_vt_children)
```


# Adults

## Samples

```{r samples adults}
bind_rows(d_us_adults, d_gh_adults, d_th_adults, d_ch_adults, d_vt_adults) %>%
  mutate(country = factor(country, levels = levels_country)) %>%
  distinct(country, subj_id) %>%
  count(country) %>%
  janitor::adorn_totals()
```

## Scale use

```{r scale use mean overall adults}
bind_rows(d_us_adults, d_gh_adults, d_th_adults, d_ch_adults, d_vt_adults) %>%
  mutate(country = factor(country, levels = levels_country),
         response_cat = recode_factor(response_cat,
                                      "no" = "no",
                                      "kind of" = "kind of",
                                      "yes" = "yes", 
                                      .missing = "missing data")) %>%
  count(country, response_cat) %>%
  complete(response_cat, nesting(country), fill = list(n = 0)) %>%
  group_by(country) %>%
  mutate(prop = n/sum(n, na.rm = T)) %>%
  ungroup() %>%
  select(-n) %>%
  spread(response_cat, prop) %>%
  janitor::adorn_pct_formatting(digits = 2)
```

## Factor retention: parallel analysis

```{r parallel dist adults, fig.width = 3, fig.asp = 0.5}
# NOTE: there appears to be some unreliability/non-reproducibility in results, especially for vt adults, which I don't understand -- so here's the distribution over outcomes of parallel analysis with 100 iterations. We'll choose the median number of factors.

if (file.exists("../results/pa_outcomes_dist_adults.RDS")) {
  
  pa_outcomes_dist_adults <- readRDS("../results/pa_outcomes_dist_adults.RDS")
  
} else {
  
  pa_outcomes_dist_adults <- data.frame(us = NULL, gh = NULL, th = NULL,
                                        ch = NULL, vt = NULL)
  
  set.seed(54321)
  n_cores <- parallel::detectCores()
  options(mc.cores = n_cores)
  
  for (i in 1:100) {
    pa_outcomes_dist_adults[i, "us"] <- fa.parallel(d_us_adults_w, plot = F)$nfact
    pa_outcomes_dist_adults[i, "gh"] <- fa.parallel(d_gh_adults_w, plot = F)$nfact     
    pa_outcomes_dist_adults[i, "th"] <- fa.parallel(d_th_adults_w, plot = F)$nfact
    pa_outcomes_dist_adults[i, "ch"] <- fa.parallel(d_ch_adults_w, plot = F)$nfact
    pa_outcomes_dist_adults[i, "vt"] <- fa.parallel(d_vt_adults_w, plot = F)$nfact
  }
  
  saveRDS(pa_outcomes_dist_adults, file = "../results/pa_outcomes_dist_adults.RDS")
}

# plot
pa_outcomes_dist_adults %>%
  rownames_to_column("iter") %>%
  gather(country, nfact, -iter) %>%
  mutate(country = factor(country,
                          levels = c("us", "gh", "th", "ch", "vt"),
                          labels = levels_country)) %>%
  ggplot(aes(x = nfact)) +
  facet_grid(~ country) +
  geom_bar(stat = "count") +
  scale_x_continuous(limits = c(1, max(pa_outcomes_dist_adults) + 1),
                     breaks = seq(0, 100, 1)) +
  labs(x = "Number of factors suggested by fa.parallel()")
```

## Exploratory factor analysis

```{r efa adults}
set.seed(54321)

# do exploratory factor analysis: adults
efa_us_adults <- fa_fun(d_us_adults_w,
                        n = median(pa_outcomes_dist_adults$us),
                        chosen_n.iter = 1000,
                        chosen_rot = "oblimin")
colnames(efa_us_adults$loadings) <- paste0("usADULTS_", 
                                           colnames(efa_us_adults$loadings))

efa_gh_adults <- fa_fun(d_gh_adults_w, 
                        n = median(pa_outcomes_dist_adults$gh),
                        chosen_n.iter = 1000,
                        chosen_rot = "oblimin")
colnames(efa_gh_adults$loadings) <- paste0("ghADULTS_", 
                                           colnames(efa_gh_adults$loadings))

efa_th_adults <- fa_fun(d_th_adults_w, 
                        n = median(pa_outcomes_dist_adults$th),
                        chosen_n.iter = 1000,
                        chosen_rot = "oblimin")
colnames(efa_th_adults$loadings) <- paste0("thADULTS_", 
                                           colnames(efa_th_adults$loadings))

efa_ch_adults <- fa_fun(d_ch_adults_w, 
                        n = median(pa_outcomes_dist_adults$ch),
                        chosen_n.iter = 1000,
                        chosen_rot = "oblimin")
colnames(efa_ch_adults$loadings) <- paste0("chADULTS_", 
                                           colnames(efa_ch_adults$loadings))

efa_vt_adults <- fa_fun(d_vt_adults_w, 
                        n = median(pa_outcomes_dist_adults$vt),
                        chosen_n.iter = 1000,
                        chosen_rot = "oblimin")
colnames(efa_vt_adults$loadings) <- paste0("vtADULTS_", 
                                           colnames(efa_vt_adults$loadings))
```

```{r factor names adults}
factor_names_adults <- data.frame(factor = c(colnames(efa_us_adults$loadings),
                                             colnames(efa_gh_adults$loadings),
                                             colnames(efa_th_adults$loadings),
                                             colnames(efa_ch_adults$loadings),
                                             colnames(efa_vt_adults$loadings))) %>%
  mutate(age_group = "adults") %>%
  mutate(country = case_when(grepl("^us", factor) ~ "US",
                             grepl("^gh", factor) ~ "Ghana",
                             grepl("^th", factor) ~ "Thailand",
                             grepl("^ch", factor) ~ "China",
                             grepl("^vt", factor) ~ "Vanuatu"),
         country = factor(country, levels_country)) %>%
  mutate(factor_name = gsub("^us", "US ", factor),
         factor_name = gsub("^gh", "Gh. ", factor_name),
         factor_name = gsub("^th", "Th. ", factor_name),
         factor_name = gsub("^ch", "Ch. ", factor_name),
         factor_name = gsub("^vt", "Va. ", factor_name),
         factor_name = gsub("ADULTS", "adults", factor_name),
         factor_name = gsub("_F", " Factor ", factor_name)) %>%
  mutate(factor_descript = recode(factor,
                                  usADULTS_F1 = "Body",
                                  usADULTS_F2 = "Heart",
                                  usADULTS_F3 = "Mind",
                                  ghADULTS_F1 = "Inner sphere (mind-like)",
                                  ghADULTS_F2 = "Body-like",
                                  ghADULTS_F3 = "Interpersonal, religious",
                                  thADULTS_F1 = "Body-like",
                                  thADULTS_F2 = "Heart-like",
                                  thADULTS_F3 = "Mind-like",
                                  chADULTS_F1 = "Heart-like",
                                  chADULTS_F2 = "Body-like",
                                  chADULTS_F3 = "Mind-like",
                                  vtADULTS_F1 = "Harmony (mind-like, heart-like)",
                                  vtADULTS_F2 = "Sin (body-like)"),
         factor_labdescript = paste(gsub(".*_F", "F", factor),
                                    factor_descript, sep = ": "))
```

## Factor loadings

```{r order adults}
# order capacities: adults
order_us_adults <- fa.sort(efa_us_adults)$loadings[] %>% rownames()
order_gh_adults <- fa.sort(efa_gh_adults)$loadings[] %>% rownames()
order_th_adults <- fa.sort(efa_th_adults)$loadings[] %>% rownames()
order_ch_adults <- fa.sort(efa_ch_adults)$loadings[] %>% rownames()
order_vt_adults <- fa.sort(efa_vt_adults)$loadings[] %>% rownames()
```

```{r loadings adults}
# compile loadings: adults
loadings_adults <- bind_rows(
  loadings_fun(efa_us_adults) %>% mutate(country = "US"),
  loadings_fun(efa_gh_adults) %>% mutate(country = "Ghana"),
  loadings_fun(efa_th_adults) %>% mutate(country = "Thailand"),
  loadings_fun(efa_ch_adults) %>% mutate(country = "China"),
  loadings_fun(efa_vt_adults) %>% mutate(country = "Vanuatu")) %>%
  mutate(country = factor(country, levels = levels_country),
         capacity_ord_us = factor(capacity, levels = order_us_adults),
         capacity_ord_gh = factor(capacity, levels = order_gh_adults),
         capacity_ord_th = factor(capacity, levels = order_th_adults),
         capacity_ord_ch = factor(capacity, levels = order_ch_adults),
         capacity_ord_vt = factor(capacity, levels = order_vt_adults)) %>%
  arrange(country, factor, desc(abs(loading)), capacity) %>%
  mutate(order = 1:nrow(.)) %>%
  left_join(factor_names_adults)
```

```{r heatmaps by site adults, fig.width = 15, fig.asp = 0.25}
plot_grid(heatmap_fun(efa_us_adults, 
                      factor_names = factor_names_adults %>%
                        filter(country == "US") %>%
                        select(factor_labdescript) %>%
                        unlist()) + 
            theme(legend.position = "none") +
            labs(title = "US: adults"),
          heatmap_fun(efa_gh_adults,
                      factor_names = factor_names_adults %>%
                        filter(country == "Ghana") %>%
                        select(factor_labdescript) %>%
                        unlist()) + 
            theme(legend.position = "none") +
            labs(title = "GHANA: adults"),
          heatmap_fun(efa_th_adults,
                      factor_names = factor_names_adults %>%
                        filter(country == "Thailand") %>%
                        select(factor_labdescript) %>%
                        unlist()) + 
            theme(legend.position = "none") +
            labs(title = "THAILAND: adults"),
          heatmap_fun(efa_ch_adults,
                      factor_names = factor_names_adults %>%
                        filter(country == "China") %>%
                        select(factor_labdescript) %>%
                        unlist()) + 
            theme(legend.position = "none") +
            labs(title = "CHINA: adults"),
          heatmap_fun(efa_vt_adults,
                      factor_names = factor_names_adults %>%
                        filter(country == "Vanuatu") %>%
                        select(factor_labdescript) %>%
                        unlist()) + 
            theme(legend.position = "none") +
            labs(title = "VANUATU: adults"),
          ncol = 5)
```

```{r heatmap adults, fig.width = 5, fig.asp = 0.7}
# make heatmap figure: adults
loadings_adults %>%
  mutate(factor_num = as.numeric(gsub(".*F", "", factor))) %>%
  mutate(sample = paste(country, "adults", sep = "\n")) %>%
  left_join(factor_names_adults) %>%
  mutate(country = factor(country, levels = levels_country)) %>%
  ggplot(aes(x = reorder(factor_labdescript, factor_num), 
             y = reorder(capacity, desc(capacity_ord_us)),
             # y = reorder(capacity, desc(capacity_ord_ec)), 
             # y = reorder(capacity, desc(capacity_ord_gh)),
             # y = reorder(capacity, desc(capacity_ord_th)),
             # y = reorder(capacity, desc(capacity_ord_ch)),
             # y = reorder(capacity, desc(capacity_ord_vt)),
             fill = loading)) +
  facet_grid(~ reorder(sample, as.numeric(country)), scales = "free", space = "free") +
  geom_tile(color = "black", size = 0.2) +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1),
                       guide = guide_colorbar(barheight = 20, barwidth = 0.5)) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
        panel.spacing.x = unit(0.8, "lines"),
        strip.text.x = element_text(size = 10, face = "bold")) +
  labs(x = NULL, y = "Capacity", fill = "Factor\nloading")
```

## Congruence

```{r congruence adults}
cong_adults <- fa.congruence(x = list(efa_us_adults$loadings,
                                      efa_gh_adults$loadings,
                                      efa_th_adults$loadings,
                                      efa_ch_adults$loadings,
                                      efa_vt_adults$loadings),
                             digits = 5) %>%
  # get_upper_tri_fun() %>%
  data.frame() %>%
  rownames_to_column("factor_A") %>%
  gather(factor_B, cong, -factor_A) %>%
  left_join(factor_names_adults %>% 
              rename_all(list(~ (paste(., "A", sep = "_"))))) %>%
  left_join(factor_names_adults %>% 
              rename_all(list(~ (paste(., "B", sep = "_")))))
```

```{r top match adults}
cong_adults_top_match_A <- top_match_fun(cong_adults, "country_A")
cong_adults_top_match_B <- top_match_fun(cong_adults, "country_B")
```

```{r cong all pairs adults, fig.width = 5, fig.asp = 0.7}
cong_adults %>%
  mutate_at(#vars(contains("labdescript")),
    vars(factor_labdescript_A),
    funs(gsub(" \\(", "\n\\(", .))) %>%
  mutate_at(#vars(contains("labdescript")),
    vars(factor_labdescript_A),
    funs(gsub("\\/", "\\/\n", .))) %>%
  # left_join(cong_adults_top_match_A %>% rename(top_match_A = top_match)) %>%
  left_join(cong_adults_top_match_B %>% rename(top_match_B = top_match)) %>%
  mutate(is_top_match = case_when(factor_A == factor_B ~ "bold.italic",
                                  # factor_A == top_match_A ~ "bold",
                                  factor_B == top_match_B ~ "bold",
                                  TRUE ~ "plain")) %>%
  # mutate(cong = ifelse(cong == 1, NA_real_, cong)) %>%
  mutate(sample_A = paste(toupper(country_A), "adults", sep = ":\n")) %>%
  mutate(sample_B = paste(toupper(country_B), "adults", sep = ":\n")) %>%
  mutate_at(vars(country_A, country_B),
            funs(factor(toupper(.), levels = toupper(levels_country)))) %>%
  ggplot(aes(x = factor_labdescript_A,
             y = reorder(factor_labdescript_B, desc(factor_labdescript_B)),
             fill = cong)) +
  facet_grid(reorder(sample_B, as.numeric(country_B)) ~ 
               reorder(sample_A, as.numeric(country_A)), 
             scales = "free", space = "free") +
  geom_tile(color = "black", size = 0.2) +
  geom_text(aes(label = case_when(is.na(cong) ~ "",
                                  TRUE ~ format(round(cong, 2), nsmall = 2)),
                fontface = is_top_match,
                color = is_top_match),
            size = 3, show.legend = F) +
  scale_color_manual(values = c("darkred", "darkblue", "black")) +
  scale_fill_viridis_c(option = "viridis", 
                       guide = guide_colorbar(barwidth = 25, barheight = 0.5)) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
        legend.position = "bottom",
        strip.text = element_text(size = 10, face = "bold")) +
  labs(x = NULL, y = NULL, fill = expression(italic(r[c])))
```

## Bootstrapped congruence

```{r bootstrap congruence adults}
if (file.exists("../results/cong_df_adults_oblique.RDS")) {
  
  cong_df_adults <- readRDS("../results/cong_df_adults_oblique.RDS")
  
} else {
  
  bs_adults <- loadings_adults %>%
    select(capacity, factor, loading) %>%
    spread(factor, loading) %>%
    select(-capacity) %>%
    sjstats::bootstrap(1000) 
  
  factors <- levels(factor(loadings_adults$factor))
  
  cong_df_adults <- data.frame(NULL)
  for (i in factors) {
    for (j in factors) {
      cname <- paste(i, j, sep = ".")
      temp <- bs_adults %>%
        mutate(cong = map_dbl(strap, ~lsa::cosine(as.data.frame(.x)[,i],
                                                  as.data.frame(.x)[,j])))
      cong_df_adults[1:1000, cname] <- temp$cong
    }
  }
  
  cong_df_adults <- cong_df_adults %>%
    gather(factor_pair, cong) %>%
    separate(factor_pair, into = c("factor_A", "factor_B"), sep = "\\.") %>%
    group_by(factor_A, factor_B) %>%
    summarise(mean = mean(cong),
              ci_lower = ci_lower(cong),
              ci_upper = ci_upper(cong)) %>%
    ungroup() %>%
    left_join(factor_names_adults %>%
                rename_all(funs(paste(., "A", sep = "_")))) %>%
    left_join(factor_names_adults %>%
                rename_all(funs(paste(., "B", sep = "_"))))
  
  rm(i, j, cname, temp, factors)
  
  saveRDS(cong_df_adults, file = "../results/cong_df_adults_oblique.RDS")
}
```

```{r cong min adults}
# find minimum value to set constant lower bound of plots
min_cong_adults <- cong_df_adults %>%
  summarise(min_cong = min(ci_lower, na.rm = T))
```

```{r cong cis us base adults, fig.width = 4, fig.asp = 0.9}
# FIGURE 3
cong_plot_fun(cong_df = cong_df_adults, which_country = "US") +
  ylim(min_cong_adults$min_cong, 1) +
  # ylim(NA, 1) +
  labs(x = NULL)
ggsave("../figures/fig03_oblique.png")
```

```{r cong cis gh base adults, fig.width = 4, fig.asp = 0.9}
# FIGURE S1
cong_plot_fun(cong_df = cong_df_adults %>%
                mutate_at(#vars(contains("labdescript")),
                  vars(factor_labdescript_A),
                  funs(gsub(" \\(", "\n\\(", .))) %>%
                mutate_at(#vars(contains("labdescript")),
                  vars(factor_labdescript_A),
                  funs(gsub("\\/", "\\/\n", .))), 
              which_country = "Ghana") +
  ylim(min_cong_adults$min_cong, 1)
ggsave("../figures/figS01_oblique.png")
```

```{r cong cis th base adults, fig.width = 4, fig.asp = 0.9}
# FIGURE S2
cong_plot_fun(cong_df = cong_df_adults, 
              which_country = "Thailand") +
  ylim(min_cong_adults$min_cong, 1)
ggsave("../figures/figS02_oblique.png")
```

```{r cong cis ch base adults, fig.width = 4, fig.asp = 0.9}
# FIGURE S3
cong_plot_fun(cong_df = cong_df_adults, 
              which_country = "China") +
  ylim(min_cong_adults$min_cong, 1)
ggsave("../figures/figS03_oblique.png")
```

```{r cong cis vt base adults, fig.width = 4, fig.asp = 0.9}
# FIGURE S4
cong_plot_fun(cong_df = cong_df_adults %>%
                mutate_at(#vars(contains("labdescript")),
                  vars(factor_labdescript_A),
                  funs(gsub(" \\(", "\n\\(", .))) %>%
                mutate_at(#vars(contains("labdescript")),
                  vars(factor_labdescript_A),
                  funs(gsub("\\/", "\\/\n", .))), 
              which_country = "Vanuatu") +
  ylim(min_cong_adults$min_cong, 1)
ggsave("../figures/figS04_oblique.png")
```

```{r body mind cong adults}
# "In each sample, there was a factor that was similar to US adults’ “body” factor...
cong_df_adults %>% 
  filter(grepl("body", tolower(factor_descript_A)), 
         grepl("body", tolower(factor_descript_B)),
         country_A != "US", country_B == "US")

# "...and not similar to the US adult “mind” factor, ...
cong_df_adults %>% 
  filter(grepl("body", tolower(factor_descript_A)), 
         grepl("mind", tolower(factor_descript_B)),
         country_A != "US", country_B == "US")

# "... and a factor that was much more similar to US adults’ “mind” factor...
cong_df_adults %>% 
  filter(grepl("mind", tolower(factor_descript_A)), 
         grepl("mind", tolower(factor_descript_B)),
         country_A != "US", country_B == "US")

# "...than the US adult “body” factor."
cong_df_adults %>% 
  filter(grepl("mind", tolower(factor_descript_A)), 
         grepl("body", tolower(factor_descript_B)),
         country_A != "US", country_B == "US")
```
```{r heart cong adults}
cong_df_adults %>% 
  filter(grepl("heart", tolower(factor_descript_A)), 
         grepl("heart", tolower(factor_descript_B)),
         country_A %in% c("Thailand", "China"), country_B == "US")

cong_df_adults %>% 
  filter(grepl("body", tolower(factor_descript_A)) | 
           grepl("mind", tolower(factor_descript_A)),
         grepl("heart", tolower(factor_descript_B)),
         country_A %in% c("Thailand", "China"), country_B == "US")
```


# Children

## Samples

```{r samples children}
bind_rows(d_us_children, d_gh_children, d_th_children, d_ch_children, d_vt_children) %>%
  mutate(country = factor(country, levels = levels_country)) %>%
  distinct(country, subj_id) %>%
  count(country) %>% 
  janitor::adorn_totals()
```

## Scale use

```{r scale use mean overall children}
bind_rows(d_us_children, d_gh_children, d_th_children, d_ch_children, d_vt_children) %>%
  mutate(country = factor(country, levels = levels_country),
         response_cat = recode_factor(response_cat,
                                      "no" = "no",
                                      "kind of" = "kind of",
                                      "yes" = "yes", 
                                      .missing = "missing data")) %>%
  count(country, response_cat) %>%
  complete(response_cat, nesting(country), fill = list(n = 0)) %>%
  group_by(country) %>%
  mutate(prop = n/sum(n, na.rm = T)) %>%
  ungroup() %>%
  select(-n) %>%
  spread(response_cat, prop) %>%
  janitor::adorn_pct_formatting(digits = 2)
```

## Factor retention: parallel analysis

```{r parallel dist children, fig.width = 3, fig.asp = 0.5}
# NOTE: there appears to be some unreliability/non-reproducibility in results, especially for vt adults, which I don't understand -- so here's the distribution over outcomes of parallel analysis with 100 iterations. We'll choose the median number of factors.

if (file.exists("../results/pa_outcomes_dist_children.RDS")) {
  
  pa_outcomes_dist_children <- readRDS("../results/pa_outcomes_dist_children.RDS")
  
} else {
  
  pa_outcomes_dist_children <- data.frame(us = NULL, gh = NULL, th = NULL,
                                          ch = NULL, vt = NULL)
  
  set.seed(54321)
  n_cores <- parallel::detectCores()
  options(mc.cores = n_cores)
  
  for (i in 1:100) {
    pa_outcomes_dist_children[i, "us"] <- fa.parallel(d_us_children_w, plot = F)$nfact
    pa_outcomes_dist_children[i, "gh"] <- fa.parallel(d_gh_children_w, plot = F)$nfact     
    pa_outcomes_dist_children[i, "th"] <- fa.parallel(d_th_children_w, plot = F)$nfact
    pa_outcomes_dist_children[i, "ch"] <- fa.parallel(d_ch_children_w, plot = F)$nfact
    pa_outcomes_dist_children[i, "vt"] <- fa.parallel(d_vt_children_w, plot = F)$nfact
  }
  
  saveRDS(pa_outcomes_dist_children, file = "../results/pa_outcomes_dist_children.RDS")
}

# plot
pa_outcomes_dist_children %>%
  rownames_to_column("iter") %>%
  gather(country, nfact, -iter) %>%
  mutate(country = factor(country,
                          levels = c("us", "gh", "th", "ch", "vt"),
                          labels = levels_country)) %>%
  ggplot(aes(x = nfact)) +
  facet_grid(~ country) +
  geom_bar(stat = "count") +
  scale_x_continuous(limits = c(1, max(pa_outcomes_dist_children) + 1),
                     breaks = seq(0, 100, 1)) +
  labs(x = "Number of factors suggested by fa.parallel()")
```

## Exploratory factor analysis

```{r efa children}
set.seed(54321)

# do exploratory factor analysis: children
efa_us_children <- fa_fun(d_us_children_w, 
                          n = median(pa_outcomes_dist_children$us),
                          chosen_n.iter = 1000,
                          chosen_rot = "oblimin")
colnames(efa_us_children$loadings) <- paste0("usCHILDREN_", 
                                             colnames(efa_us_children$loadings))

efa_gh_children <- fa_fun(d_gh_children_w,
                          n = median(pa_outcomes_dist_children$gh),
                          chosen_n.iter = 1000,
                          chosen_rot = "oblimin")
colnames(efa_gh_children$loadings) <- paste0("ghCHILDREN_", 
                                             colnames(efa_gh_children$loadings))

efa_th_children <- fa_fun(d_th_children_w, 
                          n = median(pa_outcomes_dist_children$th),
                          chosen_n.iter = 1000,
                          chosen_rot = "oblimin")
colnames(efa_th_children$loadings) <- paste0("thCHILDREN_", 
                                             colnames(efa_th_children$loadings))

efa_ch_children <- fa_fun(d_ch_children_w, 
                          n = median(pa_outcomes_dist_children$ch),
                          chosen_n.iter = 1000,
                          chosen_rot = "oblimin")
colnames(efa_ch_children$loadings) <- paste0("chCHILDREN_", 
                                             colnames(efa_ch_children$loadings))

efa_vt_children <- fa_fun(d_vt_children_w, 
                          n = median(pa_outcomes_dist_children$vt),
                          chosen_n.iter = 1000,
                          chosen_rot = "oblimin")
colnames(efa_vt_children$loadings) <- paste0("vtCHILDREN_", 
                                             colnames(efa_vt_children$loadings))
```

```{r factor names children}
factor_names_children <- data.frame(factor = c(colnames(efa_us_children$loadings),
                                               colnames(efa_gh_children$loadings),
                                               colnames(efa_th_children$loadings),
                                               colnames(efa_ch_children$loadings),
                                               colnames(efa_vt_children$loadings))) %>%
  mutate(age_group = "children") %>%
  mutate(country = case_when(grepl("^us", factor) ~ "US",
                             grepl("^gh", factor) ~ "Ghana",
                             grepl("^th", factor) ~ "Thailand",
                             grepl("^ch", factor) ~ "China",
                             grepl("^vt", factor) ~ "Vanuatu"),
         country = factor(country, levels_country)) %>%
  mutate(factor_name = gsub("^us", "US ", factor),
         factor_name = gsub("^gh", "Gh. ", factor_name),
         factor_name = gsub("^th", "Th. ", factor_name),
         factor_name = gsub("^ch", "Ch. ", factor_name),
         factor_name = gsub("^vt", "Va. ", factor_name),
         factor_name = gsub("CHILDREN", "children", factor_name),
         factor_name = gsub("_F", " Factor ", factor_name)) %>%
  mutate(factor_descript = recode(factor,
                                  usCHILDREN_F1 = "Body-like, negative",
                                  usCHILDREN_F3 = "Heart-like, positive",
                                  usCHILDREN_F2 = "Mind-like",
                                  ghCHILDREN_F1 = "Body-like, negative",
                                  ghCHILDREN_F2 = "Mind-like, positive",
                                  ghCHILDREN_F3 = "Pray, add, etc.",
                                  thCHILDREN_F1 = "Body-like, positive",
                                  thCHILDREN_F2 = "Heart-like, negative",
                                  thCHILDREN_F3 = "Mind-like",
                                  thCHILDREN_F4 = "Add, pray, etc.",
                                  chCHILDREN_F1 = "Heart-like",
                                  chCHILDREN_F2 = "Body-like",
                                  chCHILDREN_F3 = "Mind-like",
                                  chCHILDREN_F4 = "Pray, etc.",
                                  vtCHILDREN_F1 = "Body-like",
                                  vtCHILDREN_F2 = "Mind-like, positive",
                                  vtCHILDREN_F3 = "Heart-like, negative"),
         factor_labdescript = paste(gsub(".*_F", "F", factor),
                                    factor_descript, sep = ": "))
```

## Factor loadings

```{r order children}
# order capacities: children
order_us_children <- fa.sort(efa_us_children)$loadings[] %>% rownames()
order_gh_children <- fa.sort(efa_gh_children)$loadings[] %>% rownames()
order_th_children <- fa.sort(efa_th_children)$loadings[] %>% rownames()
order_ch_children <- fa.sort(efa_ch_children)$loadings[] %>% rownames()
order_vt_children <- fa.sort(efa_vt_children)$loadings[] %>% rownames()
```

```{r loadings children}
# compile loadings: children
loadings_children <- bind_rows(
  loadings_fun(efa_us_children) %>% mutate(country = "US"),
  loadings_fun(efa_gh_children) %>% mutate(country = "Ghana"),
  loadings_fun(efa_th_children) %>% mutate(country = "Thailand"),
  loadings_fun(efa_ch_children) %>% mutate(country = "China"),
  loadings_fun(efa_vt_children) %>% mutate(country = "Vanuatu")) %>%
  mutate(country = factor(country, levels = levels_country),
         capacity_ord_us = factor(capacity, levels = order_us_children),
         capacity_ord_gh = factor(capacity, levels = order_gh_children),
         capacity_ord_th = factor(capacity, levels = order_th_children),
         capacity_ord_ch = factor(capacity, levels = order_ch_children),
         capacity_ord_vt = factor(capacity, levels = order_vt_children)) %>%
  arrange(country, factor, desc(abs(loading)), capacity) %>%
  mutate(order = 1:nrow(.)) %>%
  left_join(factor_names_children)
```

```{r heatmaps by site children, fig.width = 15, fig.asp = 0.25}
plot_grid(heatmap_fun(efa_us_children, 
                      factor_names = factor_names_children %>%
                        filter(country == "US") %>%
                        select(factor_labdescript) %>%
                        unlist()) + 
            theme(legend.position = "none") +
            labs(title = "US: children"),
          heatmap_fun(efa_gh_children,
                      factor_names = factor_names_children %>%
                        filter(country == "Ghana") %>%
                        select(factor_labdescript) %>%
                        unlist()) + 
            theme(legend.position = "none") +
            labs(title = "GHANA: children"),
          heatmap_fun(efa_th_children,
                      factor_names = factor_names_children %>%
                        filter(country == "Thailand") %>%
                        select(factor_labdescript) %>%
                        unlist()) + 
            theme(legend.position = "none") +
            labs(title = "THAILAND: children"),
          heatmap_fun(efa_ch_children,
                      factor_names = factor_names_children %>%
                        filter(country == "China") %>%
                        select(factor_labdescript) %>%
                        unlist()) + 
            theme(legend.position = "none") +
            labs(title = "CHINA: children"),
          heatmap_fun(efa_vt_children,
                      factor_names = factor_names_children %>%
                        filter(country == "Vanuatu") %>%
                        select(factor_labdescript) %>%
                        unlist()) + 
            theme(legend.position = "none") +
            labs(title = "VANUATU: children"),
          ncol = 5)
```

```{r heatmap children, fig.width = 5, fig.asp = 0.7}
# make heatmap figure: children
loadings_children %>%
  mutate(factor_num = as.numeric(gsub(".*F", "", factor))) %>%
  mutate(sample = paste(country, "children", sep = "\n")) %>%
  left_join(factor_names_children) %>%
  mutate(country = factor(country, levels = levels_country)) %>%
  ggplot(aes(x = reorder(factor_labdescript, factor_num), 
             y = reorder(capacity, desc(capacity_ord_us)),
             # y = reorder(capacity, desc(capacity_ord_ec)), 
             # y = reorder(capacity, desc(capacity_ord_gh)),
             # y = reorder(capacity, desc(capacity_ord_th)),
             # y = reorder(capacity, desc(capacity_ord_ch)),
             # y = reorder(capacity, desc(capacity_ord_vt)),
             fill = loading)) +
  facet_grid(~ reorder(sample, as.numeric(country)), scales = "free", space = "free") +
  geom_tile(color = "black", size = 0.2) +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1),
                       guide = guide_colorbar(barheight = 20, barwidth = 0.5)) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
        panel.spacing.x = unit(0.8, "lines"),
        strip.text.x = element_text(size = 10, face = "bold")) +
  labs(x = NULL, y = "Capacity", fill = "Factor\nloading")
```

## Congruence

See [All samples], below.

## Bootstrapped congruence

```{r bootstrap congruence children}
if (file.exists("../results/cong_df_children_oblique.RDS")) {
  
  cong_df_children <- readRDS("../results/cong_df_children_oblique.RDS")
  
} else {
  
  bs_children <- loadings_children %>%
    select(capacity, factor, loading) %>%
    spread(factor, loading) %>%
    full_join(loadings_adults %>%
                select(capacity, factor, loading) %>%
                spread(factor, loading)) %>%
    select(-capacity) %>%
    sjstats::bootstrap(1000) 
  
  cong_df_children <- data.frame(NULL)
  
  for (k in levels_country) {
    
    factors_children <- levels(factor(loadings_children$factor[
      loadings_children$country == k]))
    factors_adults <- levels(factor(loadings_adults$factor[
      loadings_adults$country == k]))
    
    for (i in factors_children) {
      for (j in factors_adults) {
        cname <- paste(i, j, sep = ".")
        temp <- bs_children %>%
          mutate(cong = map_dbl(strap, ~lsa::cosine(as.data.frame(.x)[,i],
                                                    as.data.frame(.x)[,j])))
        cong_df_children[1:1000, cname] <- temp$cong
      }
    }
    
    rm(i, j, cname, temp, factors_children, factors_adults)
    
  }
  
  rm(k)
  
  cong_df_children <- cong_df_children %>%
    gather(factor_pair, cong) %>%
    separate(factor_pair, into = c("factor_A", "factor_B"), sep = "\\.") %>%
    group_by(factor_A, factor_B) %>%
    summarise(mean = mean(cong),
              ci_lower = ci_lower(cong),
              ci_upper = ci_upper(cong)) %>%
    ungroup() %>%
    full_join(factor_names_children %>%
                rename_all(funs(paste(., "A", sep = "_")))) %>%
    full_join(factor_names_adults %>%
                rename_all(funs(paste(., "B", sep = "_")))) %>%
    mutate(factor_bhm_A = case_when(
      grepl("body", tolower(factor_descript_A)) ~ "Body-like\nchild factor",
      grepl("mind", tolower(factor_descript_A)) ~ "Mind-like\nchild factor",
      grepl("heart", tolower(factor_descript_A)) ~ "Heart-like\nchild factor",
      TRUE ~ "Other")) %>%
    mutate(factor_bhm_B = case_when(
      grepl("body", tolower(factor_descript_B)) ~ "Local adults:\nBody-like factor",
      grepl("mind", tolower(factor_descript_B)) ~ "Local adults:\nMind-like factor",
      grepl("heart", tolower(factor_descript_B)) ~ "Local adults:\nHeart-like factor",
      TRUE ~ "Local adults:\nOther factor"))
  
  saveRDS(cong_df_children, file = "../results/cong_df_children_oblique.RDS")
}
```

```{r cong min children}
# find minimum value to set constant lower bound of plots
min_cong_children <- cong_df_children %>%
  summarise(min_cong = min(ci_lower, na.rm = T))
```

```{r cong cis children, fig.width = 4, fig.asp = 1.4}
# FIGURE 4
# fig.asp chosen to keep absolute height of y-axis relatively similar across adults and children
cong_df_children %>%
  mutate(region_A = case_when(
    country_A == "US" ~ "SF Bay Area",
    country_A == "Ghana" ~ "Cape Coast",
    country_A == "Thailand" ~ "Chiang Mai",
    country_A == "China" ~ "Shanghai",
    country_A == "Vanuatu" ~ "PV & Malekula")) %>%
  mutate(sample_A = paste(country_A, age_group_A, sep = "\n")) %>%
  mutate(lab_A = paste(paste0(region_A, ","), 
                       paste0(toupper(country_A), ":"), 
                       age_group_A, sep = "\n")) %>%
  ggplot(aes(x = factor_labdescript_A, y = mean)) +
  facet_grid(factor_bhm_B ~ reorder(lab_A, as.numeric(country_A)), 
             scales = "free_x", space = "free_x") +
  annotate("rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = 0.85,
           fill = "gray20", alpha = 0.2) +
  annotate("rect", xmin = -Inf, xmax = Inf, ymin = 0.85, ymax = 0.95,
           fill = viridisLite::viridis(2, begin = 0.75/2, end = 0.75)[1], alpha = 0.2) +
  annotate("rect", xmin = -Inf, xmax = Inf, ymin = 0.95, ymax = Inf,
           fill = viridisLite::viridis(2, begin = 0.75/2, end = 0.75)[2], alpha = 0.2) +
  geom_hline(yintercept = 0.85, lty = 2, color = "gray10") +
  geom_hline(yintercept = 0.95, lty = 2, color = "gray10") +
  geom_pointrange(aes(ymin = ci_lower, ymax = ci_upper),
                  fatten = 3,
                  show.legend = F) +
  geom_text(aes(label = format(round(mean, 2), nsmall = 2),
                y = ifelse(ci_lower < 0.2, ci_upper + 0.05, ci_lower - 0.05),
                vjust = ifelse(ci_lower < 0.2, 0, 1))) +
  scale_y_continuous(breaks = seq(-1, 1, 0.2),
                     expand = expansion(add = 0.05)) +
  scale_color_brewer(palette = "Dark2", aesthetics = c("color", "fill")) +
  scale_shape_manual(values = 21:25) +
  labs(x = NULL,
       y = expression("Similarity "(italic(r[c])))) + 
  guides(color = "none", fill = "none") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
        legend.position = "right",
        panel.border = element_rect(fill = scales::alpha("white", 0), color = "black"),
        strip.text = element_text(size = 10, face = "bold"), 
        plot.margin = unit(c(5.5, 5.5, 5.5, 15.5), "point"))
ggsave("../figures/fig04_oblique.png")
```

```{r body mind cong children}
# "In each sample, there was a factor that was much more similar to local adults’ “body-like” factor...
cong_df_children %>% 
  filter(grepl("body", tolower(factor_bhm_A)), 
         grepl("body", tolower(factor_bhm_B)))

# "...than their “mind-like” factor, ...
cong_df_children %>% 
  filter(grepl("body", tolower(factor_bhm_A)), 
         grepl("mind", tolower(factor_bhm_B)))

# "... and a factor that was much more similar to local adults’ “mind-like” factor...
cong_df_children %>% 
  filter(grepl("mind", tolower(factor_bhm_A)), 
         grepl("mind", tolower(factor_bhm_B)))

# "...than their “body-like” factor."
cong_df_children %>% 
  filter(grepl("mind", tolower(factor_bhm_A)), 
         grepl("body", tolower(factor_bhm_B)))
```


# All samples

## Congruence

```{r congruence all samples}
cong_all <- fa.congruence(x = list(efa_us_adults$loadings,
                                   efa_gh_adults$loadings,
                                   efa_th_adults$loadings,
                                   efa_ch_adults$loadings,
                                   efa_vt_adults$loadings,
                                   efa_us_children$loadings,
                                   efa_gh_children$loadings,
                                   efa_th_children$loadings,
                                   efa_ch_children$loadings,
                                   efa_vt_children$loadings),
                          digits = 5) %>%
  # get_upper_tri_fun() %>%
  data.frame() %>%
  rownames_to_column("factor_A") %>%
  gather(factor_B, cong, -factor_A) %>%
  left_join(bind_rows(factor_names_adults %>% 
                        rename_all(funs(paste(., "A", sep = "_"))),
                      factor_names_children %>%
                        rename_all(funs(paste(., "A", sep = "_"))))) %>%
  left_join(bind_rows(factor_names_adults %>% 
                        rename_all(funs(paste(., "B", sep = "_"))),
                      factor_names_children %>%
                        rename_all(funs(paste(., "B", sep = "_")))))
```

```{r cong all pairs format}
# make wide-form version of df
cong_all_w <- cong_all %>%
  select(factor_A, factor_B, cong) %>%
  spread(factor_B, cong) %>%
  column_to_rownames("factor_A")

# treat similarity matrix as if it were the correlation matrix for hclust
row.order <- hclust(as.dist((1 - cong_all_w)/2))$order
col.order <- hclust(as.dist(t((1 - cong_all_w)/2)))$order

# re-order matrix accoring to clustering
cong_all_w <- cong_all_w[row.order, col.order] 

# for some reason reshape2::melt() works better than current tidyverse functions...
cong_all_ordered <- melt(as.matrix(cong_all_w)) %>%
  rename(factor_A_ordered = Var1, 
         factor_B_ordered = Var2,
         cong = value) %>%
  mutate(factor_A = as.character(factor_A_ordered),
         factor_B = as.character(factor_B_ordered)) %>%
  full_join(cong_all %>% select(contains("_A")) %>% distinct()) %>%
  full_join(cong_all %>% select(contains("_B")) %>% distinct()) %>%
  mutate(lab_A = paste(paste(country_A, age_group_A), factor_labdescript_A, sep = ", "),
         lab_B = paste(paste(country_B, age_group_B), factor_labdescript_B, sep = ", "))
# mutate(sample_A = paste(country_A, age_group_A, sep = ", "),
#        sample_B = paste(country_B, age_group_B, sep = ", "),
#        lab_A = paste(sample_A, factor_labdescript_A, sep = " "),
#        lab_B = paste(sample_B, factor_labdescript_B, sep = " "))
```

```{r cong all pairs plot, fig.width = 9.5, fig.asp = 0.9}
# FIGURE 2
cong_lower_lim <- ifelse(min(cong_all_ordered$cong) > -0.05, -0.05, 
                         min(cong_all_ordered$cong))
# cong_plot_colors <- c("red4", "blue4", "darkorchid4", "black")
# cong_plot_colors <- c("black", "black", "black", "black")
cong_plot_colors <- c("red4", "red4", "red4", "black")

cong_all_ordered %>%
  ggplot(aes(x = reorder(lab_A, as.numeric(factor_A_ordered)),
             y = reorder(lab_B, as.numeric(desc(factor_B_ordered))),
             fill = cong)) + 
  geom_tile(color = "black", size = 0.2) +
  geom_text(aes(label = format(round(cong, 2), nsmall = 2),
                color = case_when(cong > 0.85 ~ "a", 
                                  cong > 0.75 ~ "b",
                                  cong > 0.65 ~ "c",
                                  TRUE ~ "d")),
            show.legend = F) +
  # body-like factors
  annotate("rect", xmin = 5.5, xmax = 15.5, ymin = 16.5, ymax = 26.5,
           color = cong_plot_colors[1], size = 1.5, alpha = 0) +
  # mind-like factors
  annotate("rect", xmin = 15.5, xmax = 25.5, ymin = 6.5, ymax = 16.5,
           color = cong_plot_colors[2], size = 1.5, alpha = 0) +
  # heart-like factors
  annotate("rect", xmin = 25.5, xmax = 31.5, ymin = 0.5, ymax = 6.5,
           color = cong_plot_colors[3], size = 1.5, alpha = 0) +
  # scale_fill_viridis_c(trans = scales::exp_trans(base = exp(1)),
  #                      limits = c(cong_lower_lim, 1), 
  #                      breaks = seq(cong_lower_lim, 1, 0.05),
  #                      labels = c(format(seq(cong_lower_lim, 0.8, 0.05), nsmall = 2),
  #                                 "0.85 = moderate", "0.90", 
  #                                 "0.95 = high", "1.00"),
  #                      option = "viridis",
  #                      guide = guide_colorbar(barheight = 40)) +
  scale_fill_gradientn(#trans = scales::exp_trans(base = exp(1)),
    limits = c(cong_lower_lim, 1), 
    breaks = seq(cong_lower_lim, 1, 0.05),
    labels = c(format(seq(cong_lower_lim, 0.8, 0.05), nsmall = 2),
               "0.85 = moderate", "0.90", 
               "0.95 = high", "1.00"),
    colors = viridisLite::viridis(6),
    values = c(0, 0.65, 0.75, 0.85, 0.95, 1),
    guide = guide_colorbar(barheight = 40)) +
  scale_color_manual(values = c("black", "black", "black", "gray60")) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(
      # angle = 45, hjust = 1, vjust = 1,
      angle = 90, hjust = 1, vjust = 1,
      size = size_fun(cong_all_ordered$lab_A, sizes = c(20, 14)),
      color = color_fun(cong_all_ordered$lab_A, color_list = cong_plot_colors),
      face  = face_fun(cong_all_ordered$lab_A)),
    axis.text.y = element_text(
      size = rev(size_fun(cong_all_ordered$lab_A, sizes = c(20, 14))),
      color = rev(color_fun(cong_all_ordered$lab_A, color_list = cong_plot_colors)),
      face  = rev(face_fun(cong_all_ordered$lab_A))),
    legend.title = element_text(face = "bold", size = 20),
    # axis.ticks = element_line(size = 0.5),
    axis.ticks.x = element_line(
      size = size_fun(cong_all_ordered$lab_A, sizes = c(1.5, 0.5)),
      color = color_fun(cong_all_ordered$lab_A, color_list = cong_plot_colors)),
    axis.ticks.y = element_line(
      size = rev(size_fun(cong_all_ordered$lab_A, sizes = c(1.5, 0.5))),
      color = rev(color_fun(cong_all_ordered$lab_A, color_list = cong_plot_colors))),
    axis.ticks.length = unit(0.25, "cm")) +
  labs(x = NULL, y = NULL, fill = expression(italic(r[c])))
ggsave("../figures/fig02_oblique.png")
```

## Jaccard Similarity

```{r jaccard all samples}
strong_load_all <- loadings_adults %>%
  bind_rows(loadings_children) %>%
  select(country, age_group, factor, capacity, loading) %>%
  mutate(strong_load = ifelse(loading >= 0.5, 1, 0)) %>%
  select(-loading)

cross_load_all <- strong_load_all %>%
  filter(strong_load == 1) %>%
  count(country, age_group, capacity, strong_load) %>%
  filter(n > 1) %>%
  mutate(cross_load = T) %>%
  select(country, age_group, capacity, cross_load)

strong_noncross_load_all <- strong_load_all %>%
  left_join(cross_load_all) %>%
  filter(is.na(cross_load))

jaccard_all <- strong_noncross_load_all %>%
  select(factor, capacity, strong_load) %>%
  spread(factor, strong_load) %>%
  column_to_rownames("capacity") %>%
  t() %>%
  dist(method = "binary", diag = T, upper = T) %>%
  as.matrix() %>%
  data.frame() %>%
  rownames_to_column("factor_A") %>%
  gather(factor_B, jaccard, -factor_A) %>%
  # compute similarity index instead of distance
  mutate(jaccard = 1 - jaccard) %>%
  left_join(bind_rows(factor_names_adults %>% 
                        rename_all(funs(paste(., "A", sep = "_"))),
                      factor_names_children %>%
                        rename_all(funs(paste(., "A", sep = "_"))))) %>%
  left_join(bind_rows(factor_names_adults %>% 
                        rename_all(funs(paste(., "B", sep = "_"))),
                      factor_names_children %>%
                        rename_all(funs(paste(., "B", sep = "_")))))
```

```{r jaccard all pairs format}
# make wide-form version of df
jaccard_all_w <- jaccard_all %>%
  select(factor_A, factor_B, jaccard) %>%
  spread(factor_B, jaccard) %>%
  column_to_rownames("factor_A")

# treat distance matrix as if it were the correlation matrix for hclust
row.order <- hclust(as.dist((1 - jaccard_all_w)/2))$order
col.order <- hclust(as.dist(t((1 - jaccard_all_w)/2)))$order

# re-order matrix accoring to clustering
jaccard_all_w <- jaccard_all_w[row.order, col.order] 

# for some reason reshape2::melt() works better than current tidyverse functions...
jaccard_all_ordered <- melt(as.matrix(jaccard_all_w)) %>%
  rename(factor_A_ordered = Var1, 
         factor_B_ordered = Var2,
         jaccard = value) %>%
  mutate(factor_A = as.character(factor_A_ordered),
         factor_B = as.character(factor_B_ordered)) %>%
  full_join(jaccard_all %>% select(contains("_A")) %>% distinct()) %>%
  full_join(jaccard_all %>% select(contains("_B")) %>% distinct()) %>%
  mutate(lab_A = paste(paste(country_A, age_group_A), factor_labdescript_A, sep = ", "),
         lab_B = paste(paste(country_B, age_group_B), factor_labdescript_B, sep = ", "))
# mutate(sample_A = paste(country_A, age_group_A, sep = ", "),
#        sample_B = paste(country_B, age_group_B, sep = ", "),
#        lab_A = paste(sample_A, factor_labdescript_A, sep = " "),
#        lab_B = paste(sample_B, factor_labdescript_B, sep = " "))
```

```{r jaccard all pairs plot, fig.width = 9.5, fig.asp = 0.9}
# FIGURE 2 equivalent
jaccard_lower_lim <- ifelse(min(jaccard_all_ordered$jaccard) > 0, 0, 
                         min(jaccard_all_ordered$jaccard))
# jaccard_plot_colors <- c("red4", "blue4", "darkorchid4", "black")
# jaccard_plot_colors <- c("black", "black", "black", "black")
jaccard_plot_colors <- c("red4", "red4", "red4", "black")

jaccard_all_ordered %>%
  ggplot(aes(x = reorder(lab_A, as.numeric(factor_A_ordered)),
             y = reorder(lab_B, as.numeric(desc(factor_B_ordered))),
             fill = jaccard)) + 
  geom_tile(color = "black", size = 0.2) +
  geom_text(aes(label = case_when(
    # jaccard %in% c(0, 1) ~ format(round(jaccard, 0), nsmall = 0),
    TRUE ~ format(round(jaccard, 2), nsmall = 2)),
    color = case_when(jaccard >= 0.75 ~ "a", 
                      jaccard >= 0.5 ~ "b",
                      jaccard >= 0.25 ~ "c",
                      TRUE ~ "d")),
    show.legend = F) +
  # mind-like and other factors
  annotate("rect", xmin = 0.5, xmax = 14.5, ymin = 17.5, ymax = 31.5,
           color = jaccard_plot_colors[2], size = 1.5, alpha = 0) +
  # body-like factors
  annotate("rect", xmin = 14.5, xmax = 24.5, ymin = 7.5, ymax = 17.5,
           color = jaccard_plot_colors[1], size = 1.5, alpha = 0) +
  # heart-like factors
  annotate("rect", xmin = 24.5, xmax = 31.5, ymin = 0.5, ymax = 7.5,
           color = jaccard_plot_colors[3], size = 1.5, alpha = 0) +
  scale_fill_viridis_c(#trans = scales::exp_trans(base = exp(1)),
                       limits = c(jaccard_lower_lim, 1),
                       breaks = seq(jaccard_lower_lim, 1, 0.05),
                       # labels = c(format(seq(jaccard_lower_lim, 0.8, 0.05), 
                       #                   nsmall = 2),
                       #            "0.85 = moderate", "0.90",
                       #            "0.95 = high", "1.00"),
                       option = "viridis", 
                       # direction = -1,
                       guide = guide_colorbar(barheight = 40)) +
  # scale_fill_gradientn(#trans = scales::exp_trans(base = exp(1)),
  #   limits = c(jaccard_lower_lim, 1), 
  #   breaks = seq(jaccard_lower_lim, 1, 0.05),
  #   labels = c(format(seq(jaccard_lower_lim, 0.8, 0.05), nsmall = 2),
  #              "0.85 = moderate", "0.90", 
  #              "0.95 = high", "1.00"),
  #   colors = viridisLite::viridis(6),
  #   values = c(0, 0.65, 0.75, 0.85, 0.95, 1),
  #   guide = guide_colorbar(barheight = 40)) +
  scale_color_manual(values = c("black", "black", "black", "gray60")) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(
      # angle = 45, hjust = 1, vjust = 1,
      angle = 90, hjust = 1, vjust = 1,
      size = size_fun(jaccard_all_ordered$lab_A, sizes = c(20, 14)),
      color = color_fun(jaccard_all_ordered$lab_A, color_list = jaccard_plot_colors),
      face  = face_fun(jaccard_all_ordered$lab_A)),
    axis.text.y = element_text(
      size = rev(size_fun(jaccard_all_ordered$lab_A, sizes = c(20, 14))),
      color = rev(color_fun(jaccard_all_ordered$lab_A, color_list = jaccard_plot_colors)),
      face  = rev(face_fun(jaccard_all_ordered$lab_A))),
    legend.title = element_text(face = "bold", size = 20),
    # axis.ticks = element_line(size = 0.5),
    axis.ticks.x = element_line(
      size = size_fun(jaccard_all_ordered$lab_A, sizes = c(1.5, 0.5)),
      color = color_fun(jaccard_all_ordered$lab_A, color_list = jaccard_plot_colors)),
    axis.ticks.y = element_line(
      size = rev(size_fun(jaccard_all_ordered$lab_A, sizes = c(1.5, 0.5))),
      color = rev(color_fun(jaccard_all_ordered$lab_A, color_list = jaccard_plot_colors))),
    axis.ticks.length = unit(0.25, "cm")) +
  labs(x = NULL, y = NULL, fill = "Jaccard\nsimilarity")
ggsave("../figures/fig02_oblique_jaccard.png")
```

## Developmental comparisons

```{r dev comp all sites, fig.width = 4, fig.asp = 1.2}
# FIGURE S5, FIGURE S6, FIGURE S7, FIGURE S8, FIGURE S9
plot_grid(heatmap_comp_fun(
  efa_list = list(efa_us_adults, efa_us_children), padding = F),
  dev_cong_plot_fun(cong_df_children, which_country = "US", padding = T),
  ncol = 1, rel_heights = c(2, 1.5), labels = "AUTO")
ggsave("../figures/figS05_oblique.png")

plot_grid(heatmap_comp_fun(
  efa_list = list(efa_gh_adults, efa_gh_children), padding = F),
  dev_cong_plot_fun(cong_df_children, which_country = "Ghana", padding = T),
  ncol = 1, rel_heights = c(2, 1.5), labels = "AUTO")
ggsave("../figures/figS06_oblique.png")

plot_grid(heatmap_comp_fun(
  efa_list = list(efa_th_adults, efa_th_children), padding = F),
  dev_cong_plot_fun(cong_df_children, which_country = "Thailand", padding = T),
  ncol = 1, rel_heights = c(2, 1.5), labels = "AUTO")
ggsave("../figures/figS07_oblique.png")

plot_grid(heatmap_comp_fun(
  efa_list = list(efa_ch_adults, efa_ch_children), padding = F),
  dev_cong_plot_fun(cong_df_children, which_country = "China", padding = T),
  ncol = 1, rel_heights = c(2, 1.5), labels = "AUTO")
ggsave("../figures/figS08_oblique.png")

plot_grid(heatmap_comp_fun(
  efa_list = list(efa_vt_adults, efa_vt_children), padding = F),
  dev_cong_plot_fun(cong_df_children, which_country = "Vanuatu", padding = T),
  ncol = 1, rel_heights = c(2, 1.5), labels = "AUTO")
ggsave("../figures/figS09_oblique.png")
```

```{r loadings all samples, fig.width = 6.5, fig.asp = 0.6}
# FIGURE 1
heatmap_comp_fun(list(efa_us_adults, efa_gh_adults, efa_th_adults, 
                      efa_ch_adults, efa_vt_adults, 
                      efa_us_children, efa_gh_children, efa_th_children, 
                      efa_ch_children, efa_vt_children), 
                 facet_order_vars = c("age_group", "country", "fnum"),
                 facet_lab_split = T) +
  theme(panel.spacing.x = unit(c(rep(0.2, 4), 1, rep(0.2, 4)), "line"),
        legend.position = "bottom") +
  guides(fill = guide_colorbar(barwidth = 30, barheight = 0.5, 
                               title = "Factor loading", title.vjust = 1))
ggsave("../figures/fig01_oblique.png")
```

```{r dominant factor, fig.width = 6.5, fig.asp = 0.6, include = F}
# highlighting dominant factor (ignoring cross-loadings > 0.05)
loadings_all <- loadings_adults %>%
  select(-contains("ord")) %>%
  full_join(loadings_children %>%
              select(-contains("ord")))

dom_factors_all <- loadings_all %>%
  group_by(country, age_group, capacity) %>% 
  top_n(1, abs(loading)) %>%
  ungroup() %>%
  select(country, age_group, capacity, factor, loading) %>%
  rename(dom_factor = factor,
         dom_loading = loading)

rect_df <- loadings_all %>%
  full_join(dom_factors_all) %>%
  mutate(fnum = gsub(".*_F", "F", factor)) %>%
  select(-starts_with("factor")) %>%
  spread(fnum, loading) %>%
  mutate(diff1 = abs(dom_loading) - abs(F1),
         diff2 = abs(dom_loading) - abs(F2),
         diff3 = abs(dom_loading) - abs(F3),
         diff4 = abs(dom_loading) - abs(F4)) %>%
  select(-c(dom_loading, starts_with("F"))) %>%
  gather(which_diff, diff, starts_with("diff")) %>%
  filter(diff != 0, !is.na(diff)) %>%
  group_by(country, age_group, capacity) %>%
  top_n(-1, diff) %>%
  ungroup() %>%
  mutate(any_small = diff < 0.05) %>%
  rename(factor = dom_factor) %>%
  left_join(full_join(factor_names_adults, factor_names_children))

# analog to FIGURE 1
temp_cap_order <- fa.sort(efa_us_adults)$loadings[] %>% rownames() %>% rev()

ggplot(rect_df %>%
         filter(!is.na(any_small)) %>%
         mutate(capacity = factor(capacity, levels = temp_cap_order)),
       aes(x = factor_labdescript, 
           y = capacity, 
           fill = any_small)) +
  facet_grid(~ interaction(country, age_group), space = "free", scales = "free") +
  geom_tile() +
  theme(panel.spacing.x = unit(c(rep(0.2, 4), 1, rep(0.2, 4)), "line"),
        axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
        legend.position = "bottom")
# ggsave("../figures/fig01v2_oblique.png")
```

```{r loadings all samples v2, fig.width = 6.5, fig.asp = 0.6}
# alternative to FIGURE 1
loadings_adults %>%
  bind_rows(loadings_children) %>%
  # select(-contains("_ord")) %>%
  mutate(factor_bhm = case_when(
    grepl("body", tolower(factor_descript)) ~ "BODY-like factors",
    grepl("mind", tolower(factor_descript)) ~ "MIND-like factors",
    grepl("heart", tolower(factor_descript)) ~ "HEART-like factors",
    TRUE ~ "Other")) %>%
  left_join(strong_noncross_load_all %>% 
              select(factor, capacity, strong_load, cross_load)) %>%
  mutate(font_face = case_when(
    strong_load == 1 & is.na(cross_load) ~ "bold",
    TRUE ~ "plain")) %>%
  ggplot(aes(x = reorder(paste(gsub("Factor ", "F", factor_name), 
                               factor_descript, sep = ": "), 
                         as.numeric(country)), 
             y = reorder(capacity_ord_us, desc(capacity_ord_us)),
             fill = loading)) +
  facet_grid(cols = vars(factor_bhm, age_group), 
             scales = "free", space = "free") +
  geom_tile(color = "black", size = 0.2) +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2), 
                fontface = font_face), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  theme_minimal() +
  labs(x = NULL, y = NULL) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
        panel.spacing.x = unit(c(0.2, 1, 0.2, 1, 0.2, 1, 0.2), "line"),
        legend.position = "bottom") +
  guides(fill = guide_colorbar(barwidth = 30, barheight = 0.5, 
                               title = "Factor loading", title.vjust = 1))
  # select(country, capacity, loading) %>%
  # mutate(loading = round(loading, 2)) %>%
  # spread(country, loading)
ggsave("../figures/fig01v2_oblique.png")
```

## Other metrics of similarity

```{r}
loadings_adults %>%
  filter(factor == "usADULTS_F1") %>%
  filter(loading >= 0.50)

loadings_adults %>%
  bind_rows(loadings_children) %>%
  # filter(grepl("body", factor_descript)) %>%
  select(country, factor, capacity, loading) %>%
  filter(loading >= 0.50) %>%
  count(country, factor)
```

```{r}
strong_loadings <- loadings_all %>%
  select(factor, capacity, loading) %>%
  mutate(strong_load = ifelse(loading >= 0.5, 1, 0)) %>%
  select(-loading) %>%
  spread(factor, strong_load)
```

```{r}
compare_fun <- function(x, y) {
  res <- x == y
  res <- sum(res)/length(x)
  return(res)
}
```

```{r}
compare_fun(strong_loadings$chADULTS_F1, strong_loadings$chADULTS_F2)
```

```{r}
bin_sim <- loadings_all %>%
  select(factor, capacity, loading) %>%
  mutate(strong_load = ifelse(loading >= 0.5, 1, 0)) %>%
  select(-loading) %>%
  spread(factor, strong_load) %>%
  column_to_rownames("capacity") %>%
  t() %>%
  dist(method = "binary", diag = T, upper = T) %>%
  as.matrix() %>%
  data.frame() %>%
  rownames_to_column("factor_A") %>%
  gather(factor_B, bin_sim, -factor_A)
```

```{r}
ggplot(bin_sim, 
       aes(x = factor_A, y = factor_B, fill = bin_sim)) +
  geom_tile()
```

```{r}
strong_load_fun <- function(which_base, which_bhm, which_age_group, 
                            cutoff) {
  df <- loadings_adults %>%
    bind_rows(loadings_children) %>%
    filter(grepl(which_bhm, tolower(factor_descript))) %>%
    select(capacity, factor, loading) %>%
    spread(factor, loading) %>%
    filter_at(vars(-capacity), any_vars(. > cutoff)) %>%
    gather(factor, loading, -capacity) %>%
    mutate(strong_load = ifelse(loading > cutoff, T, F)) %>%
    count(factor, strong_load) %>%
    group_by(factor) %>%
    mutate(prop_strong_load = n/sum(n)) %>%
    filter(strong_load == T) %>%
    select(factor, prop_strong_load) %>%
    left_join(full_join(factor_names_adults, factor_names_children)) %>%
    mutate(factor_bhm = case_when(
      grepl("body", tolower(factor_descript)) ~ "BODY-like factors",
      grepl("mind", tolower(factor_descript)) ~ "MIND-like factors",
      grepl("heart", tolower(factor_descript)) ~ "HEART-like factors",
      TRUE ~ "Other")) %>%
    filter(as.character(age_group) %in% which_age_group)
  
  return(df)
}
```

```{r}
strong_load_plot_fun <- function(strong_df, which_bhm) {
  g <- strong_df %>%
    ggplot(aes(x = reorder(factor, as.numeric(country)), 
               y = prop_strong_load, fill = country)) +
    facet_grid(. ~ factor_bhm, scales = "free_x", space = "free_x") +
    geom_bar(stat = "identity") +
    scale_fill_brewer(palette = "Dark2") +
    scale_y_continuous(limits = c(0, 1)) +
    labs(title = paste0(toupper(which_bhm), "-like factors")) 
  
  return(g)
}
```

```{r}
strong_load_fun(which_bhm = "body", which_age_group = "adults", cutoff = 0.5)
strong_load_fun(which_bhm = "heart", which_age_group = "adults", cutoff = 0.5)
strong_load_fun(which_bhm = "mind", which_age_group = "adults", cutoff = 0.5)
```


```{r}
strong_load_fun(which_bhm = "body", which_age_group = "adults", cutoff = 0.5) %>%
  strong_load_plot_fun(which_bhm = "body")
strong_load_fun(which_bhm = "heart", which_age_group = "adults", cutoff = 0.5)
strong_load_fun(which_bhm = "mind", which_age_group = "adults", cutoff = 0.5)
```


## Variance accounted for

```{r}
Vaccounted_fun <- function(efa_name) {
  country <- gsub("efa_", "", efa_name)
  country <- gsub("_.*$", "", country)
  age_group <- case_when(grepl("adult", efa_name) ~ "adults",
                         grepl("child", efa_name) ~ "children",
                         TRUE ~ NA_character_)
  
  efa <- get(efa_name)
  res <- efa$Vaccounted %>%
    data.frame() %>%
    rownames_to_column("metric") %>%
    mutate(country = factor(country, 
                            levels = c("us", "gh", "th", "ch", "vt"),
                            labels = levels_country),
           age_group = factor(age_group, levels = c("adults", "children")))
  
  return(res)
}
```

```{r}
Vaccounted_all <- Vaccounted_fun("efa_us_adults") %>%
  full_join(Vaccounted_fun("efa_gh_adults")) %>%
  full_join(Vaccounted_fun("efa_th_adults")) %>%
  full_join(Vaccounted_fun("efa_ch_adults")) %>%
  full_join(Vaccounted_fun("efa_vt_adults")) %>%
  full_join(Vaccounted_fun("efa_us_children")) %>%
  full_join(Vaccounted_fun("efa_gh_children")) %>%
  full_join(Vaccounted_fun("efa_th_children")) %>%
  full_join(Vaccounted_fun("efa_ch_children")) %>%
  full_join(Vaccounted_fun("efa_vt_children"))
```

```{r}
Vaccounted_all %>%
  filter(metric %in% c("Proportion Var", "Proportion Explained")) %>%
  gather(factor, value, starts_with("F")) %>%
  mutate(value = round(value, 2)) %>%
  spread(country, value) %>%
  arrange(age_group, factor, metric)
```
```{r}
Vaccounted_all %>%
  filter(metric == "Cumulative Var") %>%
  gather(factor, value, starts_with("F")) %>%
  group_by(country, age_group) %>%
  top_n(1, value) %>%
  ungroup() %>%
  mutate(value = round(value, 2)) %>%
  select(metric, country, age_group, value) %>%
  spread(country, value) %>%
  arrange(age_group, metric)
```
## Interfactor correlations

```{r}
interfactor_cor_fun <- function(efa_name) {
  sample = gsub("efa_", "", efa_name)
  country = gsub("_.*$", "", sample)
  age_group = gsub("^.*_", "", sample)
  
  efa <- get(efa_name)
  
  # hacky, not sure why this works, but it's the only way i could get CIs
  df <- print(efa) %>%
    data.frame() %>%
    rownames_to_column("factor_pair") %>%
    separate(factor_pair, c("factor_A" ,"factor_B"), sep = "-") %>%
    mutate_at(vars(starts_with("factor")), ~ gsub("^.*_", "", .))
  
  # df <- efa$Phi %>%
  #   data.frame() %>%
  #   rownames_to_column("factor_A")
  # gather(factor_B, phi, -factor_A)
  
  df <- df %>%
  mutate_at(vars(starts_with("factor")),
            ~ paste0(country, toupper(age_group), "_", .)) %>%
    mutate(country = factor(country,
                            levels = c("us", "gh", "th", "ch", "vt"),
                            labels = levels_country),
           age_group = factor(age_group, levels = c("adults", "children")))
  
  return(df)
}
```

```{r, results = "hide"}
d_phi <- bind_rows(interfactor_cor_fun("efa_us_adults"),
                   interfactor_cor_fun("efa_gh_adults"),
                   interfactor_cor_fun("efa_th_adults"),
                   interfactor_cor_fun("efa_ch_adults"),
                   interfactor_cor_fun("efa_vt_adults"),
                   interfactor_cor_fun("efa_us_children"),
                   interfactor_cor_fun("efa_gh_children"),
                   interfactor_cor_fun("efa_th_children"),
                   interfactor_cor_fun("efa_ch_children"),
                   interfactor_cor_fun("efa_vt_children"))
```
```{r}
d_phi <- d_phi %>%
  full_join(d_phi %>%
              rename_all(~ gsub("factor_A", "factor_C", .)) %>%
              rename_all(~ gsub("factor_B", "factor_D", .)) %>%
              rename_all(~ gsub("factor_D", "factor_A", .)) %>%
              rename_all(~ gsub("factor_C", "factor_B", .))) %>% distinct()
```

```{r}
d_phi <- d_phi %>%
  select(-country, -age_group) %>%
  left_join(factor_names_adults %>%
              full_join(factor_names_children) %>%
              rename_all(~paste0(., "_A"))) %>%
  mutate(factor_bhm_A = case_when(
    grepl("body", tolower(factor_descript_A)) ~ "Body-like factor",
    grepl("mind", tolower(factor_descript_A)) ~ "Mind-like factor",
    grepl("heart", tolower(factor_descript_A)) ~ "Heart-like factor",
    TRUE ~ "Other")) %>%
  left_join(factor_names_adults %>%
              full_join(factor_names_children) %>%
              rename_all(~paste0(., "_B"))) %>%
  mutate(factor_bhm_B = case_when(
    grepl("body", tolower(factor_descript_B)) ~ "Body-like factor",
    grepl("mind", tolower(factor_descript_B)) ~ "Mind-like factor",
    grepl("heart", tolower(factor_descript_B)) ~ "Heart-like factor",
    TRUE ~ "Other")) %>%
  mutate_at(vars(factor_bhm_A, factor_bhm_B),
            ~ factor(., levels = c("Body-like factor",
                                   "Heart-like factor",
                                   "Mind-like factor",
                                   "Other"))) %>%
  select(-country_B, -age_group_B) %>%
  rename(country = country_A, age_group = age_group_A)
```


```{r, fig.width = 4, fig.asp = 1}
d_phi %>%
  ggplot(aes(x = factor_bhm_A, 
             y = reorder(factor_bhm_B, desc(factor_bhm_B)), 
             fill = estimate)) +
  facet_grid(country ~ age_group, scales = "free", space = "free") +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(estimate, 2), nsmall = 2))) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
  labs(x = NULL, y = NULL, fill = quote(phi))
```
```{r, fig.width = 4, fig.asp = 0.6}
d_phi %>%
  ggplot(aes(x = factor_bhm_A, 
             color = country,
             # shape = age_group,
             y = estimate)) +
  geom_hline(yintercept = 0, lty = 5, color = "gray50") +
  facet_grid(age_group ~ factor_bhm_B, scales = "free_x", space = "free") +
  geom_pointrange(aes(ymin = lower, ymax = upper),
                  position = position_dodge(width = 0.5),
                  fatten = 2) +
  # geom_text(aes(label = format(round(estimate, 2), nsmall = 2))) +
  scale_color_brewer(palette = "Dark2") +
  scale_y_continuous(limits = c(NA, 1), 
                     breaks = seq(0, 1, 0.5)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
  labs(x = NULL, y = quote(phi))
```

```{r, fig.width = 5, fig.asp = 0.7}
d_phi %>%
  ggplot(aes(x = country, 
             color = country,
             group = age_group, shape = age_group,
             y = estimate)) +
  facet_grid(factor_bhm_A ~ factor_bhm_B, scales = "free_x", space = "free") +
  geom_hline(yintercept = 0, lty = 2, color = "grey50") +
  geom_pointrange(aes(ymin = lower, ymax = upper),
                  position = position_dodge(width = 0.5)) +
  scale_color_brewer(palette = "Dark2") +
  # geom_text(aes(y = ifelse(age_group == "adults", 
  #                          estimate + 0.1,
  #                          estimate - 0.05),
  #               label = format(round(estimate, 2), nsmall = 2)),
  #           position = position_dodge(width = 0.5)) +
  scale_y_continuous(limits = c(NA, 1), 
                     breaks = seq(0, 1, 0.5)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
  labs(x = NULL, y = quote(phi), 
       shape = "Age group", size = "Age group", 
       color = "Site")
```

```{r}
cat("US ADULTS\n")
efa_us_adults$Phi
(efa_us_adults$Phi)^2

cat("\nUS CHILDREN\n")
efa_us_children$Phi
(efa_us_children$Phi)^2
```

```{r}
cat("GHANA ADULTS\n")
efa_gh_adults$Phi
(efa_gh_adults$Phi)^2

cat("\nGHANA CHILDREN\n")
efa_gh_children$Phi
(efa_gh_children$Phi)^2
```

```{r}
cat("THAILAND ADULTS\n")
efa_th_adults$Phi
(efa_th_adults$Phi)^2

cat("\nTHAILAND CHILDREN\n")
efa_th_children$Phi
(efa_th_children$Phi)^2
```

```{r}
cat("CHINA ADULTS\n")
efa_ch_adults$Phi
(efa_ch_adults$Phi)^2

cat("\nCHINA CHILDREN\n")
efa_ch_children$Phi
(efa_ch_children$Phi)^2
```

```{r}
cat("VANUATU ADULTS\n")
efa_vt_adults$Phi
(efa_vt_adults$Phi)^2

cat("\nVANUATU CHILDREN\n")
efa_vt_children$Phi
(efa_vt_children$Phi)^2
```

```{r}

```


